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Data Enrichment Examples: Driving Better Business Results for Retail, Insurance, Telecommunications, Real Estate and More

Authors Photo Precisely Editor | April 27, 2020

You might have vast amounts of data, but how complete is it? Complete data gives you a fuller picture of your business. Moreover, having complete data helps you make truly informed business decisions, thereby helping to ensure better results. How do you achieve complete data? The answer lies in data enrichment.

“Data enrichment” means that trusted third-party data is merged with your existing customer database. 

Complete data is that which is comprehensive. In a nutshell, all the information you need is available. For example, you may not necessarily need someone’s middle name, though you do need that person’s address and zip or postal code.

How can data enrichment drive better business results? 

When your data is complete, you make better business decisions. How so? We’ll illustrate with some data enrichment examples. 

Telecommunications 

Let’s say you’re in the telecommunications industry. You decide to undertake a data enrichment project to better understand your customers as well as to determine in what direction your business should go. 

One of the ways this could help you is for network planning. Here’s how: 

  • Manage your network and assets: Using information about your competitors (such as what current assets they have and how big their market is), you can determine what gaps and opportunities exist to make better investments.
  • Optimize your network: Based on existing infrastructure and your customer data, offer optimal network coverage to your clients.
  • Gain a deeper understanding of capacity, reliability, and network operations: By combining equipment specifications and customer data, you’ll learn what your network’s capacity is.
  • Prepare for emergencies: Information from previous disasters help you determine how to get ready for the next one.

What kinds of data help you? 

  • Boundaries: Postcodes or census tracts define telcos’ customer service areas. That helps telcos evaluate where and how much to invest.
  • Granular consumer data: Information about individual customers allows telcos to target consumers effectively.
  • Demographics: Data about the local population and their characteristics facilitate decisions about asset and network investment.
  • Routing: Learn how accessible locations are for servicing and emergency planning.
  • Streets: Knowing where streets and transport networks are located has an impact on services and on asset location.
  • Local points of interest: What are some geographical points that would affect demand or business requirements?
  • Industry-specific data: Information about telecommunications enables precise planning.
Two colleagues looking at documents.

Retail

Another of our data enrichment examples comes from the retail industry. You can use data enrichment to better personalize marketing messages, for example. 

Here’s how data enrichment can help:

  • Granular customer data: Who are your customers? What are their likes and dislikes? How does this impact performance?
  • Location data: Where your customers are located enables you to segment and target them better.
  • In-store and e-commerce analytics: Patterns from brick-and-mortar stores and digital channels provide deeper understanding of what customers are buying and where.
  • Better decision making: Based on your product mix, location, and marketing data, you can improve the choices you make for more profitable results.
  • Increased traffic: The combination of customer data and geographical information boosts your near-me search rankings to attract more shoppers.

What kinds of data do you need?

  • Boundaries: Post codes and census data give retailers a  sense of which locations are performing better.
  • Customer data: Link fragmented and incomplete identities like phone number, email, social or physical address to transactional data, digital marketing activities, and data models.
  • Demographics: What impact do local population characteristics have on purchasing patterns? 
  • Routing: How far are customers willing to travel to a store? This affects store performance.
  • Local competitors: What impact do your competitors and other businesses have on your business? Are people more or less likely to visit your store because of what’s around you?
Pointing to a data chart on a tablet screen.

Insurance

The third data enrichment example comes from the insurance industry. Among the variety of use cases is one to reduce fraudulent claims. 

Insurance fraud is an expensive problem; the FBI estimates it costs $40 billion per year. However, the right data can help you stop fraud in its tracks:

  • Identify fraudulent claims: Analytics help you determine when a con is taking place.
  • Deal with claims faster: Accurate information lets you handle claims quickly and correctly.

What kind of data do you need?

  • Local and regional information: Understand the context of a claim.
  • Property attributes: An in-depth view of the property allows insurers to accurately respond to claims.
  • Risks: What natural hazards affect this claim?
  • Location data: Mapping and geocoding enrich the claim’s context.

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Changing the Rules of Data

See why companies are rethinking data-driven practices to keep pace and win in today’s digital market.

Real estate

Real estate brings us the last of the data enrichment examples. Data enrichment can be used to map properties for sale. 

Here’s how data enrichment helps: 

  • Comparative selection: Residential neighborhood boundaries enable you to segment similar areas.
  • Valuation: Calculate value based on specific local features.
  • Visualization and mapping: Locate properties accurately and visualize features that add value, such as golf courses and shopping centers.
  • Automated valuation modeling: Factoring in community and amenities leads to improved comparative selection models.

What kind of data do you need?

  • Addresses: With addresses, you can visualize address locations on a map and add relevant information.
  • Boundaries: Based on the neighborhood and community characteristics, you can appraise homes and develop automated valuation models.
  • Property attributes: In-depth views of the property creates better-automated valuation models.

Data enrichment tools 

What tools can help you carry out data enrichment? Our Enrich products power enhanced decision making with expertly curated, up-to-date business, location, and consumer data. Explore the Precisely Data Guide to find the data you need to gain insight, drive growth and minimize risk.

To understand why companies are rethinking data-driven practices to keep pace and win in today’s digital market, read Changing the Rules of Data a report from Harvard Business Review Analytic Services.